Non-parametric lane estimation in urban environments

Beck, Johannes; Stiller, Christoph

doi:10.1109/ivs.2014.6856551

Cited by 16 publications

(9 citation statements)

References 13 publications

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“…Nonparametric models are less common because they demand only that the line should be continuous. It provokes the model to be less robust than parametric models but more flexible to adapt to the irregular shapes present in urban environments [37] or rural paths [38].…”

Section: Map Road Modelmentioning

confidence: 99%

High-Level Interpretation of Urban Road Maps Fusing Deep Learning-Based Pixelwise Scene Segmentation and Digital Navigation Maps

Fernández

Muñoz-Bulnes²,

Llorca

et al. 2018

Journal of Advanced Transportation

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This paper addresses the problem of high-level road modeling for urban environments. Current approaches are based on geometric models that fit well to the road shape for narrow roads. However, urban environments are more complex and those models are not suitable for inner city intersections or other urban situations. The approach presented in this paper generates a model based on the information provided by a digital navigation map and a vision-based sensing module. On the one hand, the digital map includes data about the road type (residential, highway, intersection, etc.), road shape, number of lanes, and other context information such as vegetation areas, parking slots, and railways. On the other hand, the sensing module provides a pixelwise segmentation of the road using a ResNet-101 CNN with random data augmentation, as well as other hand-crafted features such as curbs, road markings, and vegetation. The high-level interpretation module is designed to learn the best set of parameters of a function that maps all the available features to the actual parametric model of the urban road, using a weighted F-score as a cost function to be optimized. We show that the presented approach eases the maintenance of digital maps using crowd-sourcing, due to the small number of data to send, and adds important context information to traditional road detection systems.

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Section: Map Road Modelmentioning

confidence: 99%

High-Level Interpretation of Urban Road Maps Fusing Deep Learning-Based Pixelwise Scene Segmentation and Digital Navigation Maps

Fernández

Muñoz-Bulnes²,

Llorca

et al. 2018

Journal of Advanced Transportation

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“…The strength of the texture anisotropy describes the homogeneity in a region of interest [6]. Additional information on road appearance is extracted from a filter bank, which is an array of [26,27] band-pass filters that usually have different scales and orientations. Finally, another common descriptor is the Local Binary Pattern (LBP), which describes the relationship between the evaluated point and its surrounding values.…”

Section: Road Appearancementioning

confidence: 99%

“…Thus, the model may be less robust than parametric models but more flexible to adapt to the irregular shapes present in urban environments [26] or rural paths [27]. One example of a nonparametric model is Ant Colony Optimization (ACO) for finding optimal trajectories on the image plane.…”

Section: Nonparametric Modelsmentioning

confidence: 99%

A Hybrid Vision-Map Method for Urban Road Detection

Fernández

Llorca

Sotelo

2017

Journal of Advanced Transportation

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A hybrid vision-map system is presented to solve the road detection problem in urban scenarios. The standardized use of machine learning techniques in classification problems has been merged with digital navigation map information to increase system robustness. The objective of this paper is to create a new environment perception method to detect the road in urban environments, fusing stereo vision with digital maps by detecting road appearance and road limits such as lane markings or curbs. Deep learning approaches make the system hard-coupled to the training set. Even though our approach is based on machine learning techniques, the features are calculated from different sources (GPS, map, curbs, etc.), making our system less dependent on the training set.

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“…Such features extracted by an off-line-trained classifier are closely related to the varieties and scales of the training samples. Multiple types of features were fused to overcome the drawbacks of unary features in a previous study [ 29 ]. In our work, the LSD algorithm [ 1 ] is used to extract lane segment features.…”

Section: Related Workmentioning

confidence: 99%

Efficient Lane Boundary Detection with Spatial-Temporal Knowledge Filtering

Nan

Wei

et al. 2016

Sensors

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Lane boundary detection technology has progressed rapidly over the past few decades. However, many challenges that often lead to lane detection unavailability remain to be solved. In this paper, we propose a spatial-temporal knowledge filtering model to detect lane boundaries in videos. To address the challenges of structure variation, large noise and complex illumination, this model incorporates prior spatial-temporal knowledge with lane appearance features to jointly identify lane boundaries. The model first extracts line segments in video frames. Two novel filters—the Crossing Point Filter (CPF) and the Structure Triangle Filter (STF)—are proposed to filter out the noisy line segments. The two filters introduce spatial structure constraints and temporal location constraints into lane detection, which represent the spatial-temporal knowledge about lanes. A straight line or curve model determined by a state machine is used to fit the line segments to finally output the lane boundaries. We collected a challenging realistic traffic scene dataset. The experimental results on this dataset and other standard dataset demonstrate the strength of our method. The proposed method has been successfully applied to our autonomous experimental vehicle.

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Non-parametric lane estimation in urban environments

Cited by 16 publications

References 13 publications

High-Level Interpretation of Urban Road Maps Fusing Deep Learning-Based Pixelwise Scene Segmentation and Digital Navigation Maps

High-Level Interpretation of Urban Road Maps Fusing Deep Learning-Based Pixelwise Scene Segmentation and Digital Navigation Maps

A Hybrid Vision-Map Method for Urban Road Detection

Efficient Lane Boundary Detection with Spatial-Temporal Knowledge Filtering

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